Method for detecting combustion efficiency of firewood-fired energy-saving furnace based on image processing

Abstract

The invention relates to the technical field of image data processing, in particular to a firewood-fired energy-saving furnace combustion efficiency detection method based on image processing, and the method comprises the steps: obtaining similar pixel points of pixel points in eight neighborhood directions in a flame gray level image, obtaining a similar range value of the pixel point according to the Euclidean distance between the pixel point and each corresponding similar pixel point; calculating the probability that a pixel point in the flame gray level image is an inner flame area; determining an inner flame region of the flame gray level image according to the probability that the pixel points in the flame gray level image are the inner flame region, and extracting an outer flame region of the flame gray level image by constructing a gray level co-occurrence matrix to obtain a flame region; the combustion efficiency of the firewood-fired energy-saving furnace is obtained through the flame area rectangularity of the flame gray level image and the heat utilization efficiency of the furnace body heat map, and the accuracy of the obtained combustion efficiency of the firewood-fired energy-saving furnace is effectively improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image data processing, and more specifically, to a method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing. Background Art

[0002] The wood-fired energy-saving stove is an energy-saving device, one of whose core goals is to improve energy efficiency and reduce fuel consumption. During the combustion process of the wood-fired energy-saving stove, by testing the combustion efficiency of the wood-fired energy-saving stove, it can ensure that the wood energy is fully burned to avoid waste. At the same time, ensuring that the wood is fully burned can effectively reduce the emission of pollutants (such as carbon monoxide and particulate matter). Therefore, during the research and testing phase of the wood-fired energy-saving stove, by testing the combustion efficiency, the actual effect of the energy-saving stove in energy saving compared to traditional wood-fired equipment can be accurately measured, so that subsequent research and improvements can be made based on this.

[0003] The patent application document with publication number CN119131042A in the existing scheme discloses an online monitoring and analysis method for the thermal efficiency of an industrial furnace based on infrared images. The method obtains a thermal image of the industrial furnace; obtains the segmented grayscale value of any pixel point in the thermal image on the three channels of R, G, and B; constructs a new grayscale image with the new grayscale values ​​of all pixels; and segments the new grayscale image to obtain a segmented image to monitor and analyze the thermal efficiency of the industrial furnace.

[0004] The existing solution uses a single thermal image to analyze thermal efficiency, but cannot accurately extract the dynamic characteristics of the flame during the combustion of a wood-fired energy-saving stove. The shape, size, intensity and position of the flame will change significantly over time, resulting in a low accuracy of the final segmentation result. Therefore, it is urgent to solve the problem of how to accurately extract the dynamic characteristics of the flame so as to accurately evaluate its combustion efficiency. Summary of the invention

[0005] In order to solve the above technical problem of how to accurately extract the flame characteristics with dynamic changes so as to accurately evaluate its combustion efficiency, the present invention proposes a combustion efficiency detection method of a wood-fired energy-saving stove based on image processing, which comprises the following steps: Determine the grayscale peak value in the flame grayscale image; obtain similar pixel points of the pixel point in the flame grayscale image in the eight neighborhood directions, and obtain the similarity range value of the pixel point according to the Euclidean distance between the pixel point and the corresponding similar pixel points; calculate the The probability that the i-th pixel in the flame grayscale image is the inner flame area : ; For the The grayscale peak value in the frame flame grayscale image, , Respectively The gray value and similarity range value of the i-th pixel in the frame flame gray image, For the The variance of the similarity range value of the i-th pixel in the flame grayscale image of the frame and the two frames before and after it, is an exponential function with base e, is the linear normalization function, is the absolute value symbol; the inner flame area of ​​the flame grayscale image is determined by the probability that the pixel point in the flame grayscale image is the inner flame area, and the outer flame area of ​​the flame grayscale image is extracted by constructing a gray level co-occurrence matrix to obtain the flame area; the combustion efficiency of the wood-fired energy-saving stove is obtained by the rectangularity of the flame area of ​​the flame grayscale image and the heat utilization efficiency of the furnace body heat map.

[0006] The present invention can accurately obtain the combustion efficiency of a wood-fired energy-saving stove by combining the shape change of the flame with the heat utilization efficiency. In the process of obtaining the shape change of the flame, the present invention takes into account that the flame usually changes dynamically, and the flame is composed of an outer flame area and an inner flame area. It is impossible to accurately capture the dynamic change of the flame based on a single-frame flame grayscale image; therefore, the present invention obtains the characteristics of a large area and high brightness of the inner flame area, and combines the area change of continuous image frames for dynamic analysis, reduces the interference of the reflective area on the furnace wall, and can accurately calculate the probability that each pixel point in the flame grayscale image is the inner flame area, so that the inner flame area of ​​the flame grayscale image can be accurately obtained, effectively improving the accuracy of the calculation of the combustion efficiency of the wood-fired energy-saving stove. On this basis, the present invention extracts the texture homogeneity features of the flame grayscale image by constructing a grayscale co-occurrence matrix, so that the outer flame area of ​​the flame grayscale image can be accurately obtained, and the flame area in the flame grayscale image can be accurately obtained based on the inner flame area and the outer flame area, effectively improving the accuracy of the calculation result of the combustion efficiency of the wood-fired energy-saving stove.

[0007] According to the method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing provided by the present invention, the determination of the grayscale peak value in the flame grayscale image also includes: acquiring the flame grayscale image and the furnace body heat map during the combustion process of the wood-fired energy-saving stove at each acquisition moment, and obtaining a flame image set consisting of multiple frames of continuous flame grayscale images.

[0008] The present invention takes into account that a single frame image cannot accurately reflect the dynamic changes of the flame area, and therefore prepares for the subsequent dynamic change feature extraction of the flame area by acquiring continuous image frames.

[0009] According to the method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing provided by the present invention, the grayscale peak value in the flame grayscale image is determined, including: constructing a grayscale histogram of the flame grayscale image with the grayscale value of the pixel point in the flame grayscale image as the horizontal axis and the number of pixel points corresponding to the grayscale value as the vertical axis, with the lower left corner of the grayscale histogram being the coordinate origin; traversing from the rightmost side of the grayscale histogram to determine the first local maximum value of the number of pixel points, and taking the grayscale value corresponding to the local maximum value as the grayscale peak value in the flame grayscale image; wherein the local maximum value is greater than the number of adjacent pixel points on the left and right sides in the grayscale histogram.

[0010] According to the method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing provided by the present invention, the method of obtaining similar pixel points of a pixel point in the flame grayscale image in the eight-neighborhood directions includes: continuously diffusing outward in the eight-neighborhood directions of the pixel point to obtain adjacent pixel points of the pixel point until the grayscale difference between the adjacent pixel points in the direction and the pixel point is greater than a first threshold and then stopping the acquisition, and taking the last adjacent pixel point whose grayscale difference is not greater than the first threshold as a similar pixel point of the pixel point in the direction.

[0011] The present invention takes into account that the inner flame area of ​​the flame area is located in the central area of ​​the flame area, has high brightness and changes smoothly, and is usually large in area. Therefore, by analyzing the area of ​​similar areas formed by the pixel point and the surrounding pixels with small grayscale differences, the probability that the pixel point is the inner flame area is accurately evaluated.

[0012] According to the method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing provided by the present invention, the similarity range value of the pixel point is obtained according to the Euclidean distance between the pixel point and the corresponding similar pixel points, including: taking the normalized mean of the Euclidean distances between the pixel point and the similar pixel points in eight neighborhood directions as the similarity range value of the pixel point.

[0013] According to the method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing provided by the present invention, the inner flame area of ​​the flame grayscale image is determined by the probability that the pixel point in the flame grayscale image is the inner flame area, including: taking the median of the probability that the pixel point in the flame grayscale image is the inner flame area as a comparison value, if the probability that the pixel point is the inner flame area is greater than or equal to the comparison value, then the pixel point is a pixel point in the inner flame area; using a regional growing algorithm to process the pixel points in the inner flame area in the flame grayscale image to obtain the inner flame area in the flame grayscale image.

[0014] According to the method for detecting combustion efficiency of wood-fired energy-saving stoves based on image processing provided by the present invention, the outer flame area of ​​the flame grayscale image is extracted by constructing a grayscale co-occurrence matrix, including: obtaining the grayscale mean of adjacent pixel points along the gradient change direction of the edge pixel points of the inner flame area in the flame grayscale image as a reference value for the outer flame area; using adjacent pixel points whose grayscale values ​​among the remaining pixel points except the inner flame area in the flame grayscale image have a difference with the reference value that is less than a second threshold as analysis pixel points, and constructing a grayscale co-occurrence matrix based on the analysis pixel points to obtain the outer flame area of ​​the flame grayscale image.

[0015] The present invention takes into account that although the outer flame area of ​​the flame grayscale image is mixed with the grayscale of the surrounding ash area, the texture of the outer flame area is uniform, while the texture of the ash area is more complex. Therefore, the homogeneity features are extracted by constructing a grayscale co-occurrence matrix, thereby accurately identifying the outer flame area with uniform texture.

[0016] According to the method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing provided by the present invention, the grayscale co-occurrence matrix is ​​constructed based on the analysis pixels to obtain the outer flame area of ​​the flame grayscale image, including: setting distance and direction parameters to construct the grayscale co-occurrence matrix of each analysis pixel to obtain the homogeneity value of each analysis pixel; recording the analysis pixel points whose homogeneity values ​​are greater than a third threshold as high homogeneity pixels; and using a regional growing algorithm to process the high homogeneity pixels in the flame grayscale image to obtain the outer flame area in the flame grayscale image.

[0017] According to the method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing provided by the present invention, the method for obtaining the rectangularity of the flame area of ​​a flame grayscale image and the heat utilization efficiency of a furnace body heat map comprises: obtaining a flame image set in which the flame grayscale image is located; taking the ratio of the number of pixel points in the flame area to the minimum circumscribed rectangular area of ​​the flame area as the rectangularity of the flame area; integrating the calorific value of the furnace body heat map corresponding to each frame of the flame grayscale image in the flame image set to obtain the heat output value of the wood-fired energy-saving stove; obtaining the theoretical calorific value of the wood-fired energy-saving stove based on the mass of the combustion material used in the process of acquiring the flame image set; and obtaining the heat utilization efficiency of the wood-fired energy-saving stove based on the difference between the heat output value of the wood-fired energy-saving stove and the theoretical calorific value.

[0018] According to the method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing provided by the present invention, the combustion efficiency of the wood-fired energy-saving stove is obtained by the rectangularity of the flame area of ​​the flame grayscale image and the heat utilization efficiency of the furnace body heat map, including: calculating the combustion efficiency of the wood-fired energy-saving stove: ; To improve the combustion efficiency of wood-fired energy-saving stoves, For the The rectangularity of the flame area of ​​the frame flame grayscale image, is the number of flame grayscale image frames in the flame image set, To measure the heat utilization efficiency of the wood-fired energy-saving stove, is a linear normalization function.

[0019] The present invention provides an accurate method for calculating the combustion efficiency of a wood-fired energy-saving stove. Based on the characteristics that a flame with a higher rectangularity burns more stably and the fuel can more fully contact and burn with oxygen, combined with the heat utilization efficiency of the wood-fired energy-saving stove, the interference of external factors can be effectively reduced, thereby accurately obtaining the combustion efficiency value of the wood-fired energy-saving stove.

[0020] The present invention has the following beneficial effects: Based on the above technical scheme, the combustion efficiency detection method of a wood-fired energy-saving stove based on image processing provided by the present invention can accurately obtain the combustion efficiency of a wood-fired energy-saving stove by combining the shape change of the flame with the heat utilization efficiency when obtaining the combustion efficiency detection of the wood-fired energy-saving stove. In the process of obtaining the shape change of the flame, the present invention obtains the characteristics of the large area and high brightness of the inner flame area, and combines the area change of the continuous image frame for dynamic analysis, while reducing the interference of the reflective area on the furnace wall, and can accurately calculate the probability that each pixel point in the flame grayscale image is the inner flame area, so that the inner flame area of ​​the flame grayscale image can be accurately obtained, effectively improving the accuracy of the calculation of the combustion efficiency of the wood-fired energy-saving stove. On this basis, the present invention extracts the texture homogeneity features of the flame grayscale image by extracting and constructing the grayscale co-occurrence matrix, so that the outer flame area of ​​the flame grayscale image can be accurately obtained, and the flame area in the flame grayscale image is accurately obtained based on the inner flame area and the outer flame area, effectively improving the accuracy of the calculation result of the combustion efficiency of the wood-fired energy-saving stove. BRIEF DESCRIPTION OF THE DRAWINGS

[0021] Figure 1 A flowchart of the steps of a method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing provided by an embodiment of the present invention; Figure 2 This is an example diagram of a flame grayscale image during the combustion process of a wood-fired energy-saving stove provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0022] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments.

[0023] In order to extract dynamically changing flame features in flame grayscale images and thus accurately obtain combustion efficiency detection results of wood-fired energy-saving stoves, an embodiment of the present invention discloses a method for detecting combustion efficiency of wood-fired energy-saving stoves based on image processing. This method obtains flame areas consisting of inner flame areas and outer flame areas by analyzing characteristic changes of continuous flame grayscale images, thereby effectively improving the accuracy of combustion efficiency detection results of wood-fired energy-saving stoves.

[0024] See also Figure 1 As shown, Figure 1 A flowchart of a method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing provided by an embodiment of the present invention, the method comprising the following steps: S1: Obtain a flame grayscale image from a flame image set.

[0025] For example, in an embodiment of the present invention, obtaining a flame grayscale image in a flame image set includes: obtaining a flame grayscale image during the combustion of a wood-fired energy-saving stove at each acquisition moment, and obtaining a flame image set consisting of multiple frames of continuous flame grayscale images.

[0026] Specifically, based on the preset acquisition frequency, a high-definition camera is used to capture the combustion image of the wood-fired energy-saving stove body during the test phase of the wood-fired energy-saving stove. The flame grayscale image is obtained after preprocessing. Multiple frames of continuous flame grayscale images collected within a preset period are combined into a flame image set, and the pixel points in different flame grayscale images correspond one by one.

[0027] Among them, the preprocessing can be grayscale processing, image denoising, image quality enhancement, highlighting the area of ​​interest, etc., which can be set according to actual needs, and the embodiment of the present invention does not impose too many restrictions here. The acquisition frequency can be set to 30 frames per second, and the preset period can be within 30 minutes after the wood-fired energy-saving stove starts burning, which can be set according to actual needs.

[0028] For example, grayscale processing can be achieved through weighted averaging; an adaptive filter can be used to remove random noise in the image to improve the signal-to-noise ratio; an adaptive histogram equalization process can be performed on the image to improve the contrast in the image; and sharpening technology can be used to enhance the details of the image.

[0029] For ease of processing, the sizes of the flame grayscale images at all acquisition moments can be adjusted to the same size, which can be specifically set according to actual needs.

[0030] It should be noted that based on the above steps, the flame grayscale image and flame image set corresponding to each acquisition moment can be obtained. Figure 2 As shown, Figure 2 This is an example of a flame grayscale image during the combustion process of a wood-fired energy-saving stove provided by an embodiment of the present invention, combined with Figure 2It can be seen that the flame area is composed of an inner flame area with higher brightness and an outer flame area with lower brightness. The inner flame area usually appears as a larger sheet area, while the outer flame area completely wraps the inner flame area. Based on this, the embodiment of the present invention can accurately extract the inner flame area and the outer flame area from the flame grayscale image according to their respective characteristics, that is, perform the following steps.

[0031] S2: Determine the grayscale peak value in the flame grayscale image; obtain the similarity range value of the pixel point based on the Euclidean distance between the pixel point and the corresponding similar pixels; calculate the probability that the pixel point in the flame grayscale image is the inner flame area based on the grayscale change and similarity range value of the pixel point between each frame of the flame grayscale image and the two frames before and after it.

[0032] It should be noted that if a pixel in the flame grayscale image is a pixel in the inner flame region, then in the continuous multi-frame image, the grayscale value of the pixel will be very close to the grayscale peak value of the current flame grayscale image, that is, it has a higher brightness. Based on this feature, the pixel in the inner flame region can be preliminarily screened out. Figure 2 It can be seen that in the flame grayscale image, in addition to the inner flame area of ​​the flame, there are also irregular reflective areas with higher brightness on the furnace wall. Such reflective areas are impurities produced by the combustion process of the combustion material, which are attached to the furnace wall and mixed with the brightness characteristics of the inner flame area. In addition, in the flame grayscale image, due to the dynamic change characteristics of the flame, the shape and position of the inner flame area may change in multiple consecutive frames of images, and the area will also change accordingly, while the reflective area is usually relatively stable, and the area change in the previous and subsequent consecutive image frames is small.

[0033] Therefore, in order to further distinguish the inner flame area from the furnace wall reflective area, the embodiment of the present invention can also combine the area between the pixel point and the surrounding pixels with smaller grayscale differences and the area fluctuation to jointly determine the probability that the pixel point is the inner flame area.

[0034] By way of example, in an embodiment of the present invention, determining a grayscale peak in a flame grayscale image includes: constructing a grayscale histogram of the flame grayscale image with the grayscale value of a pixel in the flame grayscale image as the horizontal axis and the number of pixels corresponding to the grayscale value as the vertical axis, with the lower left corner of the grayscale histogram being the coordinate origin; traversing from the rightmost side of the grayscale histogram to determine the first local maximum of the number of pixels, and taking the grayscale value corresponding to the local maximum as the grayscale peak in the flame grayscale image; wherein the local maximum in the grayscale histogram is greater than the number of adjacent pixels on the left and right sides.

[0035] For example, when obtaining the area between a pixel and surrounding pixels with smaller grayscale differences, similar pixels of the pixel in the flame grayscale image in eight neighborhood directions can be obtained respectively, where the similar pixels are boundary points of the similar range of the pixel in the neighborhood direction.

[0036] By way of example, in an embodiment of the present invention, similar pixel points of a pixel point in a flame grayscale image in eight-neighborhood directions are obtained, including: continuously diffusing outward in the eight-neighborhood directions of the pixel point to obtain adjacent pixel points of the pixel point until the grayscale difference between the adjacent pixel points in the direction and the pixel point is greater than a first threshold, and then stopping the acquisition, and taking the last adjacent pixel point whose grayscale difference is not greater than the first threshold as a similar pixel point of the pixel point in the direction.

[0037] The first threshold may be set to 30, and the first threshold may be set according to actual needs.

[0038] By way of example, in an embodiment of the present invention, the similarity range value of the pixel point is obtained based on the Euclidean distance between the pixel point and the corresponding similar pixel points, including: taking the normalized mean of the Euclidean distances between the pixel point and the similar pixel points in eight neighborhood directions as the similarity range value of the pixel point.

[0039] The normalization method may be the maximum and minimum normalization in the linear normalization method, and may be specifically set according to actual needs.

[0040] It can be understood that the similarity range value of a pixel point is used to characterize the size of the similarity range of the pixel point. The Euclidean distance between a pixel point and each similar pixel point in the eight neighborhood directions is the Euclidean distance between the pixel point and the boundary point of its similarity range in the neighborhood direction. The smaller the Euclidean distance, the smaller the similarity range area of ​​the pixel point, and the smaller the corresponding similarity range value.

[0041] For example, in the embodiment of the present invention, the probability that a pixel in the flame grayscale image is an inner flame area is calculated based on the grayscale change and similarity range value of the pixel between each flame grayscale image frame and the two frames before and after it. For details, see the following relationship: ; For the The probability that the i-th pixel in the frame flame grayscale image is the inner flame area, For the The grayscale peak value in the frame flame grayscale image, For the The gray value of the i-th pixel in the frame flame gray image, For the The similarity range value of the i-th pixel in the frame flame grayscale image, For the The variance of the similarity range value of the i-th pixel in the flame grayscale image of the frame and the two frames before and after it, is an exponential function with base e, is the linear normalization function, is the absolute value symbol.

[0042] Among them, The two frames before and after the flame grayscale image are divided into the first Frame flame grayscale image and Frame flame grayscale image.

[0043] In the above formula, Indicates The i-th pixel in the flame grayscale image and the The degree of closeness between the grayscale peaks in the frame flame grayscale image. The smaller the value, the closer the brightness of the pixel is to the maximum brightness, and the higher the degree of conformity to the brightness characteristics of the inner flame area. The degree of closeness is converted into probability through an exponential function. The smaller the difference, the higher the degree of closeness, and the greater the probability of corresponding to the inner flame area.

[0044] Indicates The area feature of the similarity range of the i-th pixel in the frame flame grayscale image. The larger the value, the larger the area of ​​the similarity range of the i-th pixel, and the more likely it is the flame inner flame area.

[0045] It can reflect the stability of the similarity range of the i-th pixel. For the inner flame area, the variance is larger due to its dynamic changes; while the variance of the reflective area is smaller. Therefore, the larger the value, the greater the probability that the i-th pixel is in the inner flame area.

[0046] In summary, if the grayscale value of the pixel in the flame grayscale image and the two frames before and after it is closer to the grayscale peak value, the flame grayscale image and the pixel in the two frames before and after it are larger in area and the area fluctuation is smaller, the corresponding pixel meets the characteristics of the inner flame area more highly, and the probability of being the inner flame area is greater. After obtaining the probability of each pixel in the flame grayscale image being the inner flame area based on the above steps, continue to perform the following steps.

[0047] S3: determining the inner flame region of the flame grayscale image by the probability that a pixel point in the flame grayscale image is the inner flame region, and extracting the outer flame region of the flame grayscale image by constructing a gray level co-occurrence matrix to obtain the flame region.

[0048] By way of example, in an embodiment of the present invention, the inner flame region of a flame grayscale image is determined by the probability that a pixel point in the flame grayscale image is in the inner flame region, including: taking the median of the probability that a pixel point in the flame grayscale image is in the inner flame region as a comparison value, if the probability that the pixel point is in the inner flame region is greater than or equal to the comparison value, then the pixel point is a pixel point in the inner flame region; and using a regional growing algorithm to process the pixel points in the inner flame region in the flame grayscale image to obtain the inner flame region in the flame grayscale image.

[0049] The step of processing the inner flame region pixels in the flame grayscale image by using a region growing algorithm to obtain the inner flame region in the flame grayscale image can be obtained by the prior art, and the embodiment of the present invention will not be described in detail herein.

[0050] It can be understood that by comparing the probability of the pixel points being the inner flame area based on the above steps, the inner flame area pixels can be accurately obtained, and by using the region growing algorithm to process the inner flame area pixels in the flame grayscale image, the inner flame area composed of the inner flame area pixels can be obtained. Then continue to perform the following steps to extract the outer flame area of ​​the flame grayscale image.

[0051] It should be noted that, combined with Figure 2 It can be seen that the edge of the inner flame area of ​​the flame region is connected to the outer flame area, and its brightness is low; while the ash produced by the burning of firewood is gray and has a low brightness. The outer flame area is connected to the ash area produced by the burning of firewood, and the boundary of the outer flame area cannot be accurately identified. However, the grayscale value texture of the pixels in the ash area is relatively chaotic and uneven, while the grayscale value of the pixels in the outer flame area is relatively uniform and has no obvious texture. In addition, the adjacent pixels along the gradient change direction of the edge pixels of the inner flame area usually belong to the outer flame area.

[0052] Based on this, the embodiment of the present invention can determine the adjacent pixels that may be the outer flame area based on the edge pixels of the inner flame area, and construct a grayscale co-occurrence matrix based on the adjacent pixels to extract the homogeneity features of the image except the inner flame area, thereby accurately identifying the outer flame area with uniform texture.

[0053] By way of example, in an embodiment of the present invention, the outer flame region of a flame grayscale image is extracted by constructing a grayscale co-occurrence matrix, including: obtaining the grayscale mean of adjacent pixel points along the gradient change direction of edge pixel points of an inner flame region in the flame grayscale image as a reference value for the outer flame region; taking adjacent pixel points whose grayscale values ​​among the remaining pixel points except the inner flame region in the flame grayscale image have a difference with the reference value that is less than a second threshold as analysis pixel points, and constructing a grayscale co-occurrence matrix based on the analysis pixel points to obtain the outer flame region of the flame grayscale image.

[0054] The second threshold value may be set to 20; the second threshold value may be set specifically according to actual needs, and the embodiment of the present invention does not impose too many restrictions on this.

[0055] By way of example, in an embodiment of the present invention, a grayscale co-occurrence matrix is ​​constructed based on the analysis pixels to obtain the outer flame region of the flame grayscale image, including: setting distance and direction parameters to construct the grayscale co-occurrence matrix of each analysis pixel to obtain the homogeneity value of each analysis pixel; recording the analysis pixels whose homogeneity values ​​are greater than a third threshold as high homogeneity pixels; and using a regional growing algorithm to process the high homogeneity pixels in the flame grayscale image to obtain the outer flame region in the flame grayscale image.

[0056] The distance can be set to 1 pixel; the direction parameter can be set to four directions: 0 degree, 45 degree, 90 degree and 135 degree; the distance and direction parameters can be set according to actual needs.

[0057] For example, when setting the third threshold, the homogeneity values ​​of the analysis pixels may be sorted, and the top 75% of the analysis pixels may be recorded as high homogeneity pixels; the third threshold may be specifically set according to actual needs.

[0058] The step of using a region growing algorithm to process highly homogeneous pixels in the flame grayscale image to obtain an outer flame region in the flame grayscale image can be obtained through the prior art, and the embodiment of the present invention will not be described in detail herein.

[0059] For example, after using a region growing algorithm to process highly homogeneous pixels in a flame grayscale image to obtain an outer flame region in the flame grayscale image, morphological operations can also be used to process the outer flame region. The specific steps can be implemented using existing technologies and will not be described in detail in the embodiments of the present invention.

[0060] After the inner flame area and the outer flame area of ​​each frame of the flame grayscale image are obtained based on the above steps, the combustion efficiency of the wood-fired energy-saving stove can be evaluated according to the characteristics of the flame area composed of the inner flame area and the outer flame area.

[0061] S4: The combustion efficiency of the wood-fired energy-saving stove is obtained through the flame area rectangularity of the flame grayscale image and the heat utilization efficiency of the stove body heat map.

[0062] It should be noted that when the shape of the flame area of ​​the flame grayscale image is a rectangle, it has uniform combustion and stable heat output. Therefore, the closer the shape of the flame area is to a rectangle, the more stable the combustion is and the higher the combustion efficiency is. The difference between the actual heat and theoretical heat of the wood-fired energy-saving stove can intuitively reflect its heat utilization efficiency. The higher the heat utilization efficiency, the higher the combustion efficiency of the corresponding wood-fired energy-saving stove.

[0063] Based on this, the embodiment of the present invention can accurately obtain the combustion efficiency of the wood-fired energy-saving stove according to the rectangularity of the flame area of ​​the flame grayscale image and the heat utilization efficiency of the stove body heat map.

[0064] For example, in an embodiment of the present invention, before obtaining the heat utilization efficiency of the furnace body heat map, it also includes: obtaining the furnace body heat map during the combustion process of the wood-fired energy-saving stove at each acquisition moment, and the flame grayscale image and the furnace body heat map at each acquisition moment correspond one by one.

[0065] The furnace body heat map may be captured by an infrared thermal imager. The acquisition frequency and cycle of the furnace body heat map may be specifically referred to the above-mentioned method for acquiring the flame grayscale image, which will not be described in detail in the embodiment of the present invention.

[0066] By way of example, in an embodiment of the present invention, a method for obtaining the rectangularity of the flame area of ​​a flame grayscale image and the heat utilization efficiency of a furnace body heat map includes: obtaining a flame image set in which the flame grayscale image is located; taking the ratio of the number of pixels in the flame area to the area of ​​the minimum circumscribed rectangle of the flame area as the rectangularity of the flame area; integrating the calorific values ​​of all furnace body heat maps corresponding to each frame of the flame grayscale image in the flame image set to obtain a heat output value of the wood-fired energy-saving stove; obtaining a theoretical calorific value of the wood-fired energy-saving stove based on the mass of the combustion material used in the process of acquiring the flame image set; and obtaining the heat utilization efficiency of the wood-fired energy-saving stove based on the difference between the heat output value of the wood-fired energy-saving stove and the theoretical calorific value.

[0067] The number of pixels in the flame area is the area of ​​the flame area.

[0068] Specifically, when obtaining the heat utilization efficiency of the wood-fired energy-saving stove based on the difference between the heat output value and the theoretical calorific value of the wood-fired energy-saving stove, the ratio of the absolute value of the difference between the heat output value and the theoretical calorific value to the theoretical calorific value can be used as the heat utilization efficiency of the wood-fired energy-saving stove.

[0069] Among them, when obtaining the theoretical calorific value of the wood-fired energy-saving stove based on the mass of the combustion material used in the process of collecting the flame image set, the calorific value of the combustion material can be obtained, and the product of the calorific value of the combustion material and the total mass of the combustion can be used as the theoretical calorific value. The specific steps can be obtained through the existing technology, and the embodiments of the present invention will not be repeated here.

[0070] It is understandable that the higher the heat utilization efficiency of the wood-fired energy-saving stove, the more complete the combustion of the wood-fired energy-saving stove, the less heat loss caused, and the higher the corresponding combustion efficiency.

[0071] For example, in the embodiment of the present invention, the combustion efficiency of the wood-fired energy-saving stove is calculated, and the following relationship can be specifically referred to: ; To improve the combustion efficiency of wood-fired energy-saving stoves, For the The rectangularity of the flame area of ​​the frame flame grayscale image, is the number of flame grayscale image frames in the flame image set, To measure the heat utilization efficiency of the wood-fired energy-saving stove, is a linear normalization function.

[0072] In the above formula, The closer the rectangularity of the flame area in the flame grayscale image is to 1, the The closer the flame area of ​​the frame flame grayscale image is to a rectangle, the more stable the combustion is. Indicates flame image concentration The mean rectangularity of the flame area in the frame flame grayscale image is used to characterize the overall stability of the flame during the combustion process. The stability value is quantified between 0 and 1 by using the maximum and minimum normalization in the linear normalization function. The larger the value, the more stable the shape change state of the flame area, the more stable the combustion state, and the higher the corresponding combustion efficiency.

[0073] In summary, if the heat utilization efficiency of the wood-fired energy-saving stove is higher and the combustion state is more stable, its combustion efficiency will be higher.

[0074] It can be seen that in the embodiment of the present invention, when obtaining the combustion efficiency of the wood-fired energy-saving stove, the grayscale peak value in the flame grayscale image can be determined; similar pixel points of the pixel point in the flame grayscale image in the eight-neighborhood direction are obtained, and the similarity range value of the pixel point is obtained according to the Euclidean distance between the pixel point and the corresponding similar pixel points; the first The probability that the i-th pixel in the flame grayscale image is the inner flame area : ; For the The grayscale peak value in the frame flame grayscale image, , Respectively The gray value and similarity range value of the i-th pixel in the frame flame gray image, For the The variance of the similarity range value of the i-th pixel in the flame grayscale image of the frame and the two frames before and after it, is an exponential function with base e, is the linear normalization function, is the absolute value symbol; the inner flame area of ​​the flame grayscale image is determined by the probability that the pixel point in the flame grayscale image is the inner flame area, and the outer flame area of ​​the flame grayscale image is extracted by constructing a grayscale co-occurrence matrix to obtain the flame area; the combustion efficiency of the wood-fired energy-saving stove is obtained by the rectangularity of the flame area of ​​the flame grayscale image and the heat utilization efficiency of the furnace body heat map, which effectively improves the accuracy of the combustion efficiency of the obtained wood-fired energy-saving stove.

[0075] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing, characterized in that: include: Determine the grayscale peak value in the flame grayscale image; Obtain similar pixel points of a pixel point in the flame grayscale image in the eight-neighborhood direction, and obtain the similarity range value of the pixel point according to the Euclidean distance between the pixel point and each corresponding similar pixel point; Calculate the The probability that the i-th pixel in the flame grayscale image is the inner flame area : ； For the The grayscale peak value in the frame flame grayscale image, , Respectively The gray value and similarity range value of the i-th pixel in the frame flame gray image, For the The variance of the similarity range value of the i-th pixel in the flame grayscale image of the frame and the two frames before and after it, is an exponential function with base e, is the linear normalization function, is the absolute value symbol; the inner flame area of ​​the flame grayscale image is determined by the probability that the pixel point in the flame grayscale image is the inner flame area, and the outer flame area of ​​the flame grayscale image is extracted by constructing a gray level co-occurrence matrix to obtain the flame area; the combustion efficiency of the wood-fired energy-saving stove is obtained by the rectangularity of the flame area of ​​the flame grayscale image and the heat utilization efficiency of the furnace body heat map.

2. The method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing according to claim 1 is characterized in that: The step of determining the grayscale peak value in the flame grayscale image also includes: The flame grayscale image and furnace body heat map of the wood-fired energy-saving stove during the combustion process are acquired at each acquisition moment, and a flame image set consisting of multiple frames of continuous flame grayscale images is obtained.

3. The method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing according to claim 1 is characterized in that: Determining the grayscale peak value in the flame grayscale image includes: The grayscale value of the pixel in the flame grayscale image is taken as the horizontal axis, and the number of pixels corresponding to the grayscale value is taken as the vertical axis to construct the grayscale histogram of the flame grayscale image, and the lower left corner of the grayscale histogram is the coordinate origin; starting from the rightmost side of the grayscale histogram, the first local maximum value of the number of pixels is determined, and the grayscale value corresponding to the local maximum value is taken as the grayscale peak value in the flame grayscale image; wherein, the local maximum value is greater than the number of adjacent pixels on the left and right sides in the grayscale histogram.

4. The method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing according to claim 1 is characterized in that: The step of obtaining similar pixel points of the pixel points in the flame grayscale image in eight neighborhood directions includes: The adjacent pixel points of the pixel point are continuously acquired by diffusing outward in the eight-neighborhood direction of the pixel point until the grayscale difference between the adjacent pixel points in this direction and the pixel point is greater than the first threshold, and the acquisition is stopped. The last adjacent pixel point whose grayscale difference is not greater than the first threshold is taken as the similar pixel point of the pixel point in this direction.

5. The method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing according to claim 1 is characterized in that: The obtaining the similarity range value of the pixel point according to the Euclidean distance between the pixel point and corresponding similar pixel points includes: The normalized mean of the Euclidean distances between the pixel point and similar pixels in the eight neighborhood directions is taken as the similarity range value of the pixel point.

6. The method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing according to claim 1 is characterized in that: The step of determining the inner flame region of the flame grayscale image by the probability that a pixel point in the flame grayscale image is an inner flame region comprises: The median of the probability that a pixel point in the flame grayscale image is in the inner flame area is taken as a comparison value. If the probability that a pixel point is in the inner flame area is greater than or equal to the comparison value, then the pixel point is a pixel point in the inner flame area. The region growing algorithm is used to process the pixels in the inner flame area in the flame grayscale image to obtain the inner flame area in the flame grayscale image.

7. The method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing according to claim 1 is characterized in that: The step of extracting the outer flame region of the flame grayscale image by constructing a grayscale co-occurrence matrix comprises: Obtain the grayscale mean value of the adjacent pixel points of the edge pixel points of the inner flame area in the flame grayscale image along the gradient change direction as the reference value of the outer flame area; The adjacent pixels whose grayscale values ​​differ from the reference value less than a second threshold value among the remaining pixels in the flame grayscale image except the inner flame area are taken as analysis pixels, and a grayscale co-occurrence matrix is ​​constructed based on the analysis pixels to obtain the outer flame area of ​​the flame grayscale image.

8. The method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing according to claim 7 is characterized in that: The step of constructing a gray level co-occurrence matrix based on the analyzed pixels to obtain the outer flame area of ​​the flame gray image includes: The distance and direction parameters are set to construct the grayscale co-occurrence matrix of each analyzed pixel point to obtain the homogeneity value of each analyzed pixel point; the analyzed pixels whose homogeneity values ​​are greater than the third threshold are recorded as high homogeneity pixels; the high homogeneity pixels in the flame grayscale image are processed using the regional growing algorithm to obtain the outer flame area in the flame grayscale image.

9. The method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing according to claim 1 is characterized in that: The method for obtaining the rectangularity of the flame area of ​​the flame grayscale image and the heat utilization efficiency of the furnace body heat map includes: The flame image set in which the flame grayscale image is located is obtained; the ratio of the number of pixels in the flame area to the area of ​​the minimum circumscribed rectangle of the flame area is taken as the rectangularity of the flame area; the calorific value of the furnace body heat map corresponding to each frame of the flame grayscale image in the flame image set is integrated to obtain the heat output value of the wood-fired energy-saving stove; the theoretical calorific value of the wood-fired energy-saving stove is obtained according to the mass of the combustion material used in the process of collecting the flame image set; the heat utilization efficiency of the wood-fired energy-saving stove is obtained according to the difference between the heat output value of the wood-fired energy-saving stove and the theoretical calorific value.

10. The method for detecting combustion efficiency of a wood-fired energy-saving stove based on image processing according to claim 9 is characterized in that: The combustion efficiency of the wood-fired energy-saving stove is obtained by the flame area rectangularity of the flame grayscale image and the heat utilization efficiency of the stove body heat map, including: Calculate the combustion efficiency of a wood-fired energy-saving stove: ； To improve the combustion efficiency of wood-fired energy-saving stoves, For the The rectangularity of the flame area of ​​the frame flame grayscale image, is the number of flame grayscale image frames in the flame image set, To measure the heat utilization efficiency of the wood-fired energy-saving stove, is a linear normalization function.

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